Deep learning reconstruction CT for liver metastases: low-dose dual-energy vs standard-dose single-energy

Lyu, Peijie; Li, Zhen; Chen, Yan; Wang, Huixia; Liu, Nana; Jie, Liu; Zhan, Pengchao; Liu, Xing; Shang, Bo; Wang, Luotong; Gao, Jianbo

doi:10.1007/s00330-023-10033-3

Cited by 15 publications

(2 citation statements)

References 33 publications

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“…As a result, general image quality metrics such as noise and CNR measured on traditional CT phantoms cannot fully capture the clinical imaging performance of DLR (Samei et al 2019 ). Second, clinical imaging studies using patient data are often limited by sample size and restricted by radiation dose exposure concerns (Akagi et al 2019 , Greffier et al 2023a , Lyu et al 2023 ), which limit the acceptable dose range as well as the number of times a patient can be scanned. Furthermore, patient scans do not have reliable ground truth images for comparison and thus cannot be used to assess the structural accuracy of a reconstructed image.…”

Section: Introductionmentioning

confidence: 99%

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm

Im,

Halliburton,

Mei

et al. 2024

Phys. Med. Biol.

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ObjectiveDeep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.MethodThe lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error (RMSE), structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.ResultsDLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25-83% in the small phantom and by 50-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR. ConclusionDLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

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Section: Introductionmentioning

confidence: 99%

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm

Im,

Halliburton,

Mei

et al. 2024

Phys. Med. Biol.

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show abstract

“…As a result, general image quality metrics such as noise and CNR measured on traditional CT phantoms cannot fully capture the clinical imaging performance of DLR. Second, clinical imaging studies using patient data are often limited by sample size and restricted by radiation dose exposure concerns 24,25,27 , which limit the acceptable dose range as well as the number of times a patient can be scanned. Furthermore, patient scans do not have reliable ground truth images for comparison and thus cannot be used to assess the structural accuracy of a reconstructed image.…”

Section: Introductionmentioning

confidence: 99%

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm

Im,

Halliburton,

Mei

et al. 2023

Preprint

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ObjectiveDeep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.ApproachThe lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different sized extension rings to mimic a small and medium sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error (RMSE), structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.Main ResultsDLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25-83% in the small phantom and by 50-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR with a non-anatomical physics phantom.SignificanceDLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

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Dual-energy CT applications on liver imaging: what radiologists and radiographers should know? A systematic review

Asmundo,

Rizzetto,

Srinivas Rao

et al. 2024

Abdom Radiol

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Deep learning reconstruction CT for liver metastases: low-dose dual-energy vs standard-dose single-energy

Cited by 15 publications

References 33 publications

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm

Dual-energy CT applications on liver imaging: what radiologists and radiographers should know? A systematic review

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